Quantum Computing and Visualization Research Challenges and Opportunities
- URL: http://arxiv.org/abs/2601.07872v1
- Date: Sun, 11 Jan 2026 00:31:38 GMT
- Title: Quantum Computing and Visualization Research Challenges and Opportunities
- Authors: E. Wes Bethel, Roel Van Beeumen, Talita Perciano,
- Abstract summary: This article examines research challenges and opportunities along the path from initial feasibility to practical use of QC platforms applied to meaningful problems.<n>From the perspective of the field of visualization, this article examines research challenges and opportunities along the path from initial feasibility to practical use of QC platforms applied to meaningful problems.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quantum computing (QC) has experienced rapid growth in recent years with the advent of robust programming environments, readily accessible software simulators and cloud-based QC hardware platforms, and growing interest in learning how to design useful methods that leverage this emerging technology for practical applications. From the perspective of the field of visualization, this article examines research challenges and opportunities along the path from initial feasibility to practical use of QC platforms applied to meaningful problems.
Related papers
- Quantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learning [40.71697366438106]
We review existing contributions regarding quantum circuit-based learning models for classical data analysis.<n>We discuss the efforts on noise-resilient and hardware-efficient QML that could enhance its practicality under current hardware limitations.
arXiv Detail & Related papers (2026-01-19T12:52:25Z) - Quantum-enhanced Computer Vision: Going Beyond Classical Algorithms [50.573955644831386]
Quantum-enhanced Computer Vision (QeCV) is a new research field at the intersection of computer vision, machine learning and quantum computing.<n>It has high potential to transform how visual signals are processed and interpreted with the help of quantum computing.<n>This survey contributes to the existing literature on QeCV with a holistic review of this research field.
arXiv Detail & Related papers (2025-10-08T17:59:51Z) - SeQUeNCe GUI: An Extensible User Interface for Discrete Event Quantum Network Simulations [55.2480439325792]
SeQUeNCe is an open source simulator of quantum network communication.<n>We implement a graphical user interface which maintains the core principles of SeQUeNCe.
arXiv Detail & Related papers (2025-01-15T19:36:09Z) - Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits [48.33631905972908]
We introduce an innovative approach that utilizes pre-trained neural networks to enhance Variational Quantum Circuits (VQC)
This technique effectively separates approximation error from qubit count and removes the need for restrictive conditions.
Our results extend to applications such as human genome analysis, demonstrating the broad applicability of our approach.
arXiv Detail & Related papers (2024-11-13T12:03:39Z) - Advancing Quantum Software Engineering: A Vision of Hybrid Full-Stack Iterative Model [5.465644852381506]
This paper proposes a hybrid full-stack iterative model that integrates quantum and classical computing.<n>It presents a comprehensive lifecycle for quantum software development, encompassing quantum-agnostic coding, testing, deployment, cloud computing services, orchestration, translation, execution, and interpretation phases.
arXiv Detail & Related papers (2024-03-18T11:18:33Z) - Quantum Computing Enhanced Service Ecosystem for Simulation in Manufacturing [56.61654656648898]
We propose a framework for a quantum computing-enhanced service ecosystem for simulation in manufacturing.
We analyse two high-value use cases with the aim of a quantitative evaluation of these new computing paradigms for industrially-relevant settings.
arXiv Detail & Related papers (2024-01-19T11:04:14Z) - Quantum Computing and Visualization: A Disruptive Technological Change
Ahead [0.753179862869346]
The focus of this article is to explore ideas related to how visualization helps in understanding Quantum Computing (QC)
QC is emerging as a promising pathway to overcome the growth limits in classical computing.
visualization has played a role in QC by providing the means to show representations of the quantum state of single-qubits in superposition states and multiple-qubits in entangled states.
arXiv Detail & Related papers (2023-10-07T22:57:04Z) - Towards Quantum Federated Learning [80.1976558772771]
Quantum Federated Learning aims to enhance privacy, security, and efficiency in the learning process.
We aim to provide a comprehensive understanding of the principles, techniques, and emerging applications of QFL.
As the field of QFL continues to progress, we can anticipate further breakthroughs and applications across various industries.
arXiv Detail & Related papers (2023-06-16T15:40:21Z) - Evolution of Quantum Computing: A Systematic Survey on the Use of
Quantum Computing Tools [5.557009030881896]
We conduct a systematic survey and categorize papers, tools, frameworks, platforms that facilitate quantum computing.
We discuss the current essence, identify open challenges and provide future research direction.
We conclude that scores of frameworks, tools and platforms are emerged in the past few years, improvement of currently available facilities would exploit the research activities in the quantum research community.
arXiv Detail & Related papers (2022-04-04T21:21:12Z) - Snowmass White Paper: Quantum Computing Systems and Software for
High-energy Physics Research [3.4654477035437328]
We identify challenges and opportunities for developing quantum computing systems and software to advance high-energy physics research.
We describe opportunities for the focused development of algorithms, applications, software, hardware, and infrastructure to support both practical and theoretical applications of quantum computing to HEP problems within the next 10 years.
arXiv Detail & Related papers (2022-03-14T13:23:20Z) - Towards Machine Learning for Placement and Routing in Chip Design: a
Methodological Overview [72.79089075263985]
Placement and routing are two indispensable and challenging (NP-hard) tasks in modern chip design flows.
Machine learning has shown promising prospects by its data-driven nature, which can be of less reliance on knowledge and priors.
arXiv Detail & Related papers (2022-02-28T06:28:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.